{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAd865lNzZpT"
   },
   "source": [
    "#  <span style=\"color:orange\">Regression Tutorial (REG101) - Level Beginner</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXN8UznszZpc"
   },
   "source": [
    "**Created using: PyCaret 2.2** <br />\n",
    "**Date Updated: November 25, 2020**\n",
    "\n",
    "# 1.0  Tutorial Objective\n",
    "Welcome to Regression Tutorial (REG101) - Level Beginner. This tutorial assumes that you are new to PyCaret and looking to get started with Regression using the `pycaret.regression` Module.\n",
    "\n",
    "In this tutorial we will learn:\n",
    "\n",
    "\n",
    "* **Getting Data:**  How to import data from PyCaret repository\n",
    "* **Setting up Environment:**  How to setup an experiment in PyCaret and get started with building regression models\n",
    "* **Create Model:**  How to create a model, perform cross validation and evaluate regression metrics\n",
    "* **Tune Model:**  How to automatically tune the hyperparameters of a regression model\n",
    "* **Plot Model:**  How to analyze model performance using various plots\n",
    "* **Finalize Model:** How to finalize the best model at the end of the experiment\n",
    "* **Predict Model:**  How to make prediction on new / unseen data\n",
    "* **Save / Load Model:**  How to save / load a model for future use\n",
    "\n",
    "Read Time : Approx. 30 Minutes\n",
    "\n",
    "\n",
    "## 1.1 Installing PyCaret\n",
    "The first step to get started with PyCaret is to install PyCaret. Installation is easy and will only take a few minutes. Follow the instructions below:\n",
    "\n",
    "#### Installing PyCaret in Local Jupyter Notebook\n",
    "`pip install pycaret`  <br />\n",
    "\n",
    "#### Installing PyCaret on Google Colab or Azure Notebooks\n",
    "`!pip install pycaret`\n",
    "\n",
    "\n",
    "## 1.2 Pre-Requisites\n",
    "- Python 3.6 or greater\n",
    "- PyCaret 2.0 or greater\n",
    "- Internet connection to load data from pycaret's repository\n",
    "- Basic Knowledge of Regression\n",
    "\n",
    "## 1.3 For Google Colab Users:\n",
    "If you are running this notebook on Google colab, run the following code at top of your notebook to display interactive visuals.<br/>\n",
    "<br/>\n",
    "`from pycaret.utils import enable_colab` <br/>\n",
    "`enable_colab()`\n",
    "\n",
    "## 1.4 See also:\n",
    "- __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__\n",
    "- __[Regression Tutorial (REG103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Expert%20-%20REG103.ipynb)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HuEUiXXhzZpi"
   },
   "source": [
    "# 2.0 What is Regression?\n",
    "\n",
    "Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable', or 'target') and one or more independent variables (often called 'features', 'predictors', or 'covariates'). The objective of regression in machine learning is to predict continuous values such as sales amount, quantity, temperature etc.\n",
    "\n",
    "__[Learn More about Regression](https://hbr.org/2015/11/a-refresher-on-regression-analysis)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xnEk7n5ZzZpm"
   },
   "source": [
    "# 3.0 Overview of the Regression Module in PyCaret\n",
    "PyCaret's Regression module (`pycaret.regression`) is a supervised machine learning module which is used for predicting continuous values / outcomes using various techniques and algorithms. Regression can be used for predicting values / outcomes such as sales, units sold, temperature or any number which is continuous.\n",
    "\n",
    "PyCaret's regression module has over 25 algorithms and 10 plots to analyze the performance of models. Be it hyper-parameter tuning, ensembling or advanced techniques like stacking, PyCaret's regression module has it all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uN95Uqo6zZpq"
   },
   "source": [
    "# 4.0 Dataset for the Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Guj8GFIJzZpu"
   },
   "source": [
    "For this tutorial we will use a dataset based on a case study called **\"Sarah Gets a Diamond\"**. This case was presented in the first year decision analysis course at Darden School of Business (University of Virginia). The basis for the data is a case regarding a hopeless romantic MBA student choosing the right diamond for his bride-to-be, Sarah. The data contains 6000 records for training. Short descriptions of each column are as follows:\n",
    "\n",
    "- **ID:** Uniquely identifies each observation (diamond)\n",
    "- **Carat Weight:** The weight of the diamond in metric carats. One carat is equal to 0.2 grams, roughly the same weight as a paperclip\n",
    "- **Cut:** One of five values indicating the cut of the diamond in the following order of desirability (Signature-Ideal, Ideal, Very Good, Good, Fair)\n",
    "- **Color:** One of six values indicating the diamond's color in the following order of desirability (D, E, F - Colorless, G, H, I - Near colorless)\n",
    "- **Clarity:** One of seven values indicating the diamond's clarity in the following order of desirability (F - Flawless, IF - Internally Flawless, VVS1 or VVS2 - Very, Very Slightly Included, or VS1 or VS2 - Very Slightly Included, SI1 - Slightly Included)\n",
    "- **Polish:** One of four values indicating the diamond's polish (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Symmetry:** One of four values indicating the diamond's symmetry (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Report:** One of of two values \"AGSL\" or \"GIA\" indicating which grading agency reported the qualities of the diamond qualities\n",
    "- **Price:** The amount in USD that the diamond is valued `Target Column`\n",
    "\n",
    "\n",
    "#### Dataset Acknowledgement:\n",
    "This case was prepared by Greg Mills (MBA ’07) under the supervision of Phillip E. Pfeifer, Alumni Research Professor of Business Administration. Copyright (c) 2007 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved.\n",
    "\n",
    "The original dataset and description can be __[found here.](https://github.com/DardenDSC/sarah-gets-a-diamond)__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wwUzzm1YzZpz"
   },
   "source": [
    "# 5.0 Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFCSZ_NKzZp3"
   },
   "source": [
    "You can download the data from the original source __[found here](https://github.com/DardenDSC/sarah-gets-a-diamond)__ and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__ or you can use PyCaret's data respository to load the data using the `get_data()` function (This will require internet connection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:12:42.891205Z",
     "start_time": "2020-04-23T13:12:42.168861Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "H6qS5U--zZp7",
    "outputId": "2a11a81c-7e67-425a-a3ef-091d2c9fbd30"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.10</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.91</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight    Cut Color Clarity Polish Symmetry Report  Price\n",
       "0          1.10  Ideal     H     SI1     VG       EX    GIA   5169\n",
       "1          0.83  Ideal     H     VS1     ID       ID   AGSL   3470\n",
       "2          0.85  Ideal     H     SI1     EX       EX    GIA   3183\n",
       "3          0.91  Ideal     E     SI1     VG       VG    GIA   4370\n",
       "4          0.83  Ideal     G     SI1     EX       EX    GIA   3171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "dataset = get_data('diamond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:12:45.180048Z",
     "start_time": "2020-04-23T13:12:45.176712Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "D5PerU66zZqK",
    "outputId": "2fdd6ab8-7d68-4cc4-81a7-0cb82ed70799"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape of data\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7eWmeLvYzZqY"
   },
   "source": [
    "In order to demonstrate the `predict_model()` function on unseen data, a sample of 600 records has been withheld from the original dataset to be used for predictions. This should not be confused with a train/test split as this particular split is performed to simulate a real life scenario. Another way to think about this is that these 600 records are not available at the time when the machine learning experiment was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:13:11.922396Z",
     "start_time": "2020-04-23T13:13:11.912627Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "R4K9F7BXzZqc",
    "outputId": "22b1c4e7-a1e1-48d2-8ddc-907e716d5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (5400, 8)\n",
      "Unseen Data For Predictions: (600, 8)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.sample(frac=0.9, random_state=786)\n",
    "data_unseen = dataset.drop(data.index)\n",
    "\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data_unseen.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DxnJV14BzZqq"
   },
   "source": [
    "# 6.0 Setting up Environment in PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "15-blMPOzZqw"
   },
   "source": [
    "The `setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in pycaret. It takes two mandatory parameters: a pandas dataframe and the name of the target column. All other parameters are optional and are used to customize the pre-processing pipeline (we will see them in later tutorials).\n",
    "\n",
    "When `setup()` is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. The data type should be inferred correctly but this is not always the case. To account for this, PyCaret displays a table containing the features and their inferred data types after `setup()` is executed. If all of the data types are correctly identified `enter` can be pressed to continue or `quit` can be typed to end the expriment. Ensuring that the data types are correct is of fundamental importance in PyCaret as it automatically performs a few pre-processing tasks which are imperative to any machine learning experiment. These tasks are performed differently for each data type which means it is very important for them to be correctly configured.\n",
    "\n",
    "In later tutorials we will learn how to overwrite PyCaret's inferred data type using the `numeric_features` and `categorical_features` parameters in `setup()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:16:02.144917Z",
     "start_time": "2020-04-23T13:15:57.427822Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "colab_type": "code",
    "id": "7V2FN4KQzZrA",
    "outputId": "43d8d23d-ef08-438a-8cc3-ba78e9773aca",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_54850d62_2f66_11eb_a23b_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow1_col1\" class=\"data row1 col1\" >Price</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow2_col1\" class=\"data row2 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow3_col1\" class=\"data row3 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow4_col1\" class=\"data row4 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow5_col1\" class=\"data row5 col1\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow8_col1\" class=\"data row8 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow9_col1\" class=\"data row9 col1\" >(3779, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow10_col1\" class=\"data row10 col1\" >(1621, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow11_col1\" class=\"data row11 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow14_col1\" class=\"data row14 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow16_col1\" class=\"data row16 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow19_col1\" class=\"data row19 col1\" >4114</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow23_col1\" class=\"data row23 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow25_col1\" class=\"data row25 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow27_col1\" class=\"data row27 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow28_col1\" class=\"data row28 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow29_col1\" class=\"data row29 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow30_col1\" class=\"data row30 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow32_col1\" class=\"data row32 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow35_col1\" class=\"data row35 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow36_col1\" class=\"data row36 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow42_col0\" class=\"data row42 col0\" >Clustering</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow43_col0\" class=\"data row43 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow43_col1\" class=\"data row43 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow44_col0\" class=\"data row44 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow44_col1\" class=\"data row44 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow45_col0\" class=\"data row45 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow45_col1\" class=\"data row45 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow46_col0\" class=\"data row46 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow47_col0\" class=\"data row47 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow48_col0\" class=\"data row48 col0\" >Group Features</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow48_col1\" class=\"data row48 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow49_col0\" class=\"data row49 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow49_col1\" class=\"data row49 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow50_col0\" class=\"data row50 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow50_col1\" class=\"data row50 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow51_col0\" class=\"data row51 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow51_col1\" class=\"data row51 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow52_col0\" class=\"data row52 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow52_col1\" class=\"data row52 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow53_col0\" class=\"data row53 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow53_col1\" class=\"data row53 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow54_col0\" class=\"data row54 col0\" >Transform Target</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow54_col1\" class=\"data row54 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54850d62_2f66_11eb_a23b_482ae32b83dalevel0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow55_col0\" class=\"data row55 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_54850d62_2f66_11eb_a23b_482ae32b83darow55_col1\" class=\"data row55 col1\" >box-cox</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
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       "<pandas.io.formats.style.Styler at 0x1bb3ed83848>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "exp_reg101 = setup(data = data, target = 'Price', session_id=123) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWBypX32zZrP"
   },
   "source": [
    "Once the setup has been succesfully executed it prints the information grid which contains several important pieces of information. Most of the information is related to the pre-processing pipeline which is constructed when `setup()` is executed. The majority of these features are out of scope for the purposes of this tutorial. However, a few important things to note at this stage include:\n",
    "\n",
    "- **session_id :**  A pseudo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment, the `session_id` is set as `123` for later reproducibility.<br/>\n",
    "<br/>\n",
    "- **Original Data :**  Displays the original shape of dataset. In this experiment (5400, 8) means 5400 samples and 8 features including the target column. <br/>\n",
    "<br/>\n",
    "- **Missing Values :**  When there are missing values in the original data, this will show as True. For this experiment there are no missing values in the dataset.<br/>\n",
    "<br/>\n",
    "- **Numeric Features :**  Number of features inferred as numeric. In this dataset, 1 out of 8 features are inferred as numeric. <br/>\n",
    "<br/>\n",
    "- **Categorical Features :**  Number of features inferred as categorical. In this dataset, 6 out of 8 features are inferred as categorical. <br/>\n",
    "<br/>\n",
    "- **Transformed Train Set :** Displays the shape of the transformed training set. Notice that the original shape of (5400, 8) is transformed into (3779, 28) for the transformed train set. The number of features has increased from 8 from 28 due to categorical encoding <br/>\n",
    "<br/>\n",
    "- **Transformed Test Set :** Displays the shape of transformed test/hold-out set. There are 1621 samples in test/hold-out set. This split is based on the default value of 70/30 that can be changed using `train_size` parameter in setup. <br/>\n",
    "\n",
    "Notice how a few tasks that are imperative to perform modeling are automatically handled, such as missing value imputation (in this case there are no missing values in training data, but we still need imputers for unseen data), categorical encoding etc. Most of the parameters in `setup()` are optional and used for customizing the pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to the intermediate and expert levels, we will cover them in much greater detail.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xBqHzabEzZrT"
   },
   "source": [
    "# 7.0 Comparing All Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QHiNl6UmzZrW"
   },
   "source": [
    "Comparing all models to evaluate performance is the recommended starting point for modeling once the setup is completed (unless you exactly know what kind of model you need, which is often not the case). This function trains all models in the model library and scores them using k-fold cross validation for metric evaluation. The output prints a score grid that shows average MAE, MSE, RMSE, R2, RMSLE and MAPE accross the folds (10 by default) along with training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:21:22.884291Z",
     "start_time": "2020-04-23T13:20:11.108031Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "atJfMGD6zZrb",
    "outputId": "af936da0-cc93-4429-ef61-20d940e0aa4e",
    "scrolled": false
   },
   "outputs": [
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       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
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       "            background-color:  lightgrey;\n",
       "        }</style><table id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col1\" class=\"data row0 col1\" >630.8795</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col2\" class=\"data row0 col2\" >2082270.6589</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col3\" class=\"data row0 col3\" >1373.9172</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.9804</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0672</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.0492</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow0_col7\" class=\"data row0 col7\" >3.1110</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >et</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col1\" class=\"data row1 col1\" >762.0118</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col2\" class=\"data row1 col2\" >2763999.1585</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col3\" class=\"data row1 col3\" >1612.2410</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.9729</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.0817</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.0607</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow1_col7\" class=\"data row1 col7\" >0.5460</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >xgboost</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col1\" class=\"data row2 col1\" >708.8427</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col2\" class=\"data row2 col2\" >2799609.3438</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col3\" class=\"data row2 col3\" >1607.9791</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.9724</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0743</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.0541</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow2_col7\" class=\"data row2 col7\" >0.4300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col1\" class=\"data row3 col1\" >760.6304</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col2\" class=\"data row3 col2\" >2929683.1860</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col3\" class=\"data row3 col3\" >1663.0148</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.9714</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0818</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.0597</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow3_col7\" class=\"data row3 col7\" >0.4810</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col1\" class=\"data row4 col1\" >752.6446</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col2\" class=\"data row4 col2\" >3056347.8515</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col3\" class=\"data row4 col3\" >1687.9907</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.9711</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0773</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.0567</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow4_col7\" class=\"data row4 col7\" >0.0410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >gbr</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col1\" class=\"data row5 col1\" >920.0795</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col2\" class=\"data row5 col2\" >3763128.6878</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col3\" class=\"data row5 col3\" >1900.8431</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.9633</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.1024</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.0770</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow5_col7\" class=\"data row5 col7\" >0.1340</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >dt</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col0\" class=\"data row6 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col1\" class=\"data row6 col1\" >1003.1237</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col2\" class=\"data row6 col2\" >5305620.3379</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col3\" class=\"data row6 col3\" >2228.7271</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.9476</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.1083</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.0775</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow6_col7\" class=\"data row6 col7\" >0.0130</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col1\" class=\"data row7 col1\" >2413.5705</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col2\" class=\"data row7 col2\" >14120491.9000</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col3\" class=\"data row7 col3\" >3726.1643</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.8621</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6689</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.2875</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow7_col7\" class=\"data row7 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >lasso</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col0\" class=\"data row8 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col1\" class=\"data row8 col1\" >2412.1923</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col2\" class=\"data row8 col2\" >14246798.1000</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col3\" class=\"data row8 col3\" >3744.2305</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.8608</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.6767</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.2866</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow8_col7\" class=\"data row8 col7\" >0.0180</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >llar</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col0\" class=\"data row9 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col1\" class=\"data row9 col1\" >2355.6152</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col2\" class=\"data row9 col2\" >14272020.5106</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col3\" class=\"data row9 col3\" >3745.3095</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8607</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6391</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.2728</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow9_col7\" class=\"data row9 col7\" >0.0090</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >br</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col0\" class=\"data row10 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col1\" class=\"data row10 col1\" >2415.8031</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col2\" class=\"data row10 col2\" >14270771.8397</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col3\" class=\"data row10 col3\" >3746.9951</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.8606</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6696</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.2873</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow10_col7\" class=\"data row10 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col0\" class=\"data row11 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col1\" class=\"data row11 col1\" >2418.7037</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col2\" class=\"data row11 col2\" >14279369.9000</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col3\" class=\"data row11 col3\" >3748.9580</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.8604</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.6690</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.2879</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow11_col7\" class=\"data row11 col7\" >0.5240</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >huber</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col1\" class=\"data row12 col1\" >1936.1465</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col2\" class=\"data row12 col2\" >18599243.6579</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col3\" class=\"data row12 col3\" >4252.8771</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col4\" class=\"data row12 col4\" >0.8209</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col5\" class=\"data row12 col5\" >0.4333</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col6\" class=\"data row12 col6\" >0.1657</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow12_col7\" class=\"data row12 col7\" >0.0500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >par</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col0\" class=\"data row13 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col1\" class=\"data row13 col1\" >1944.1634</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col2\" class=\"data row13 col2\" >19955672.9330</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col3\" class=\"data row13 col3\" >4400.2133</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col4\" class=\"data row13 col4\" >0.8083</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col5\" class=\"data row13 col5\" >0.4317</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col6\" class=\"data row13 col6\" >0.1594</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow13_col7\" class=\"data row13 col7\" >0.0180</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >omp</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col0\" class=\"data row14 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col1\" class=\"data row14 col1\" >2792.7313</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col2\" class=\"data row14 col2\" >23728654.4124</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col3\" class=\"data row14 col3\" >4829.3171</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col4\" class=\"data row14 col4\" >0.7678</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col5\" class=\"data row14 col5\" >0.5818</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col6\" class=\"data row14 col6\" >0.2654</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow14_col7\" class=\"data row14 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >ada</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col0\" class=\"data row15 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col1\" class=\"data row15 col1\" >4232.2217</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col2\" class=\"data row15 col2\" >25201423.0703</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col3\" class=\"data row15 col3\" >5012.4175</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col4\" class=\"data row15 col4\" >0.7467</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col5\" class=\"data row15 col5\" >0.5102</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col6\" class=\"data row15 col6\" >0.5970</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow15_col7\" class=\"data row15 col7\" >0.1190</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >knn</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col0\" class=\"data row16 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col1\" class=\"data row16 col1\" >2968.0751</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col2\" class=\"data row16 col2\" >29627912.2000</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col3\" class=\"data row16 col3\" >5421.7240</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col4\" class=\"data row16 col4\" >0.7051</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col5\" class=\"data row16 col5\" >0.3664</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col6\" class=\"data row16 col6\" >0.2730</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow16_col7\" class=\"data row16 col7\" >0.0420</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >en</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col0\" class=\"data row17 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col1\" class=\"data row17 col1\" >5029.5912</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col2\" class=\"data row17 col2\" >56399797.2000</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col3\" class=\"data row17 col3\" >7467.6598</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col4\" class=\"data row17 col4\" >0.4472</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col5\" class=\"data row17 col5\" >0.5369</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col6\" class=\"data row17 col6\" >0.5845</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow17_col7\" class=\"data row17 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >lar</th>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col0\" class=\"data row18 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col1\" class=\"data row18 col1\" >8999.4587</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col2\" class=\"data row18 col2\" >1790407941.5151</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col3\" class=\"data row18 col3\" >17144.0046</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col4\" class=\"data row18 col4\" >-21.2868</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col5\" class=\"data row18 col5\" >0.7959</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col6\" class=\"data row18 col6\" >1.2898</td>\n",
       "                        <td id=\"T_7bb70a6c_2f66_11eb_9abf_482ae32b83darow18_col7\" class=\"data row18 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb25233788>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = compare_models(exclude = ['ransac'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "epD0BEVyzZrr"
   },
   "source": [
    "Two simple words of code ***(not even a line)*** have trained and evaluated over 20 models using cross validation. The score grid printed above highlights the highest performing metric for comparison purposes only. The grid by default is sorted using `R2` (highest to lowest) which can be changed by passing `sort` parameter. For example `compare_models(sort = 'RMSLE')` will sort the grid by RMSLE (lower to higher since lower is better). If you want to change the fold parameter from the default value of `10` to a different value then you can use the `fold` parameter. For example `compare_models(fold = 5)` will compare all models on 5 fold cross validation. Reducing the number of folds will improve the training time. By default, compare_models return the best performing model based on default sort order but can be used to return a list of top N models by using `n_select` parameter. </br>\n",
    "\n",
    "Notice that how `exclude` parameter is used to block certain models (in this case `RANSAC`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZzpBazV1zZrx"
   },
   "source": [
    "# 8.0 Create a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IPqPRp5OzZr1"
   },
   "source": [
    "`create_model` is the most granular function in PyCaret and is often the foundation behind most of the PyCaret functionalities. As the name suggests this function trains and evaluates a model using cross validation that can be set with fold parameter. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. \n",
    "\n",
    "For the remaining part of this tutorial, we will work with the below models as our candidate models. The selections are for illustration purposes only and do not necessarily mean they are the top performing or ideal for this type of data.\n",
    "\n",
    "- AdaBoost Regressor ('ada')\n",
    "- Light Gradient Boosting Machine ('lightgbm') \n",
    "- Decision Tree\t ('dt')\n",
    "\n",
    "There are 25 regressors available in the model library of PyCaret. To see list of all regressors either check the docstring or use `models` function to see the library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>sklearn.linear_model._base.LinearRegression</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lasso</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>sklearn.linear_model._coordinate_descent.Lasso</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>sklearn.linear_model._ridge.Ridge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en</th>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>sklearn.linear_model._coordinate_descent.Elast...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lar</th>\n",
       "      <td>Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model._least_angle.Lars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llar</th>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model._least_angle.LassoLars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omp</th>\n",
       "      <td>Orthogonal Matching Pursuit</td>\n",
       "      <td>sklearn.linear_model._omp.OrthogonalMatchingPu...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>br</th>\n",
       "      <td>Bayesian Ridge</td>\n",
       "      <td>sklearn.linear_model._bayes.BayesianRidge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ard</th>\n",
       "      <td>Automatic Relevance Determination</td>\n",
       "      <td>sklearn.linear_model._bayes.ARDRegression</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>par</th>\n",
       "      <td>Passive Aggressive Regressor</td>\n",
       "      <td>sklearn.linear_model._passive_aggressive.Passi...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ransac</th>\n",
       "      <td>Random Sample Consensus</td>\n",
       "      <td>sklearn.linear_model._ransac.RANSACRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>TheilSen Regressor</td>\n",
       "      <td>sklearn.linear_model._theil_sen.TheilSenRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>sklearn.linear_model._huber.HuberRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kr</th>\n",
       "      <td>Kernel Ridge</td>\n",
       "      <td>sklearn.kernel_ridge.KernelRidge</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>Support Vector Regression</td>\n",
       "      <td>sklearn.svm._classes.SVR</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Regressor</td>\n",
       "      <td>sklearn.neighbors._regression.KNeighborsRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>sklearn.tree._classes.DecisionTreeRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>sklearn.ensemble._forest.RandomForestRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Regressor</td>\n",
       "      <td>sklearn.ensemble._forest.ExtraTreesRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>sklearn.ensemble._weight_boosting.AdaBoostRegr...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbr</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>sklearn.ensemble._gb.GradientBoostingRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>MLP Regressor</td>\n",
       "      <td>pycaret.internal.tunable.TunableMLPRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>xgboost.sklearn.XGBRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>lightgbm.sklearn.LGBMRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>catboost.core.CatBoostRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name  \\\n",
       "ID                                            \n",
       "lr                        Linear Regression   \n",
       "lasso                      Lasso Regression   \n",
       "ridge                      Ridge Regression   \n",
       "en                              Elastic Net   \n",
       "lar                  Least Angle Regression   \n",
       "llar           Lasso Least Angle Regression   \n",
       "omp             Orthogonal Matching Pursuit   \n",
       "br                           Bayesian Ridge   \n",
       "ard       Automatic Relevance Determination   \n",
       "par            Passive Aggressive Regressor   \n",
       "ransac              Random Sample Consensus   \n",
       "tr                       TheilSen Regressor   \n",
       "huber                       Huber Regressor   \n",
       "kr                             Kernel Ridge   \n",
       "svm               Support Vector Regression   \n",
       "knn                   K Neighbors Regressor   \n",
       "dt                  Decision Tree Regressor   \n",
       "rf                  Random Forest Regressor   \n",
       "et                    Extra Trees Regressor   \n",
       "ada                      AdaBoost Regressor   \n",
       "gbr             Gradient Boosting Regressor   \n",
       "mlp                           MLP Regressor   \n",
       "xgboost           Extreme Gradient Boosting   \n",
       "lightgbm    Light Gradient Boosting Machine   \n",
       "catboost                 CatBoost Regressor   \n",
       "\n",
       "                                                  Reference  Turbo  \n",
       "ID                                                                  \n",
       "lr              sklearn.linear_model._base.LinearRegression   True  \n",
       "lasso        sklearn.linear_model._coordinate_descent.Lasso   True  \n",
       "ridge                     sklearn.linear_model._ridge.Ridge   True  \n",
       "en        sklearn.linear_model._coordinate_descent.Elast...   True  \n",
       "lar                  sklearn.linear_model._least_angle.Lars   True  \n",
       "llar            sklearn.linear_model._least_angle.LassoLars   True  \n",
       "omp       sklearn.linear_model._omp.OrthogonalMatchingPu...   True  \n",
       "br                sklearn.linear_model._bayes.BayesianRidge   True  \n",
       "ard               sklearn.linear_model._bayes.ARDRegression  False  \n",
       "par       sklearn.linear_model._passive_aggressive.Passi...   True  \n",
       "ransac         sklearn.linear_model._ransac.RANSACRegressor  False  \n",
       "tr        sklearn.linear_model._theil_sen.TheilSenRegressor  False  \n",
       "huber            sklearn.linear_model._huber.HuberRegressor   True  \n",
       "kr                         sklearn.kernel_ridge.KernelRidge  False  \n",
       "svm                                sklearn.svm._classes.SVR  False  \n",
       "knn       sklearn.neighbors._regression.KNeighborsRegressor   True  \n",
       "dt              sklearn.tree._classes.DecisionTreeRegressor   True  \n",
       "rf           sklearn.ensemble._forest.RandomForestRegressor   True  \n",
       "et             sklearn.ensemble._forest.ExtraTreesRegressor   True  \n",
       "ada       sklearn.ensemble._weight_boosting.AdaBoostRegr...   True  \n",
       "gbr          sklearn.ensemble._gb.GradientBoostingRegressor   True  \n",
       "mlp            pycaret.internal.tunable.TunableMLPRegressor  False  \n",
       "xgboost                        xgboost.sklearn.XGBRegressor   True  \n",
       "lightgbm                     lightgbm.sklearn.LGBMRegressor   True  \n",
       "catboost                    catboost.core.CatBoostRegressor   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wxKHHQcbzZr5"
   },
   "source": [
    "### 8.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:30.940588Z",
     "start_time": "2020-04-23T13:22:28.992149Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "-NVGDCR3zZr8",
    "outputId": "06f5fc68-d2a5-4b59-fea2-3661ea1cb29d"
   },
   "outputs": [
    {
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       "                <tr>\n",
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       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7955</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.5068</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.5871</td>\n",
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       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
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       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.5089</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.5960</td>\n",
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       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow4_col0\" class=\"data row4 col0\" >3888.5584</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow4_col1\" class=\"data row4 col1\" >24461807.7242</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow4_col2\" class=\"data row4 col2\" >4945.8880</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.6949</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.4764</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5461</td>\n",
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       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow5_col0\" class=\"data row5 col0\" >4566.4889</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow5_col1\" class=\"data row5 col1\" >29733914.8752</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow5_col2\" class=\"data row5 col2\" >5452.8813</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7462</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.5462</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6598</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow6_col0\" class=\"data row6 col0\" >4628.7271</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow6_col1\" class=\"data row6 col1\" >27841092.1974</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow6_col2\" class=\"data row6 col2\" >5276.4659</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7384</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.5549</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6676</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow7_col0\" class=\"data row7 col0\" >4316.4317</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow7_col1\" class=\"data row7 col1\" >25979752.0083</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow7_col2\" class=\"data row7 col2\" >5097.0336</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.6715</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.5034</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.5858</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow8_col0\" class=\"data row8 col0\" >3931.2163</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow8_col1\" class=\"data row8 col1\" >21097072.3513</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow8_col2\" class=\"data row8 col2\" >4593.1549</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7928</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.4858</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5513</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow9_col0\" class=\"data row9 col0\" >4291.1097</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow9_col1\" class=\"data row9 col1\" >24815566.0009</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow9_col2\" class=\"data row9 col2\" >4981.5225</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.7637</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.5495</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6592</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow10_col0\" class=\"data row10 col0\" >4232.2217</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow10_col1\" class=\"data row10 col1\" >25201423.0703</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow10_col2\" class=\"data row10 col2\" >5012.4175</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7467</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.5102</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5970</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ca38740_2f66_11eb_829f_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow11_col0\" class=\"data row11 col0\" >233.2282</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow11_col1\" class=\"data row11 col1\" >2804219.3826</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow11_col2\" class=\"data row11 col2\" >277.6577</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0375</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0284</td>\n",
       "                        <td id=\"T_7ca38740_2f66_11eb_829f_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0457</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb4111b988>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = create_model('ada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:38.337909Z",
     "start_time": "2020-04-23T13:22:38.335103Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "NHL2zciizZsI",
    "outputId": "d606ad03-ecd5-487b-b205-bfc7a841f3a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
      "                  n_estimators=50, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "print(ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-dvDHxCzZsU"
   },
   "source": [
    "### 8.2 Light Gradient Boosting Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:46.292921Z",
     "start_time": "2020-04-23T13:22:43.713857Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "NC7OVDVrzZsX",
    "outputId": "a5abc702-d270-4134-892a-e9ecf82bdebb"
   },
   "outputs": [
    {
     "data": {
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       "                <tr>\n",
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       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow1_col0\" class=\"data row1 col0\" >797.6185</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow1_col1\" class=\"data row1 col1\" >5638866.1771</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow1_col2\" class=\"data row1 col2\" >2374.6297</td>\n",
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       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.0537</td>\n",
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       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow2_col0\" class=\"data row2 col0\" >829.4586</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow2_col1\" class=\"data row2 col1\" >3328375.4390</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow2_col2\" class=\"data row2 col2\" >1824.3836</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9695</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.0860</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0619</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow3_col0\" class=\"data row3 col0\" >720.3923</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow3_col1\" class=\"data row3 col1\" >1697211.3816</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow3_col2\" class=\"data row3 col2\" >1302.7707</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9813</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.0714</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0554</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow4_col0\" class=\"data row4 col0\" >645.6800</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow4_col1\" class=\"data row4 col1\" >1799949.1196</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow4_col2\" class=\"data row4 col2\" >1341.6218</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.9775</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.0745</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0534</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow5_col0\" class=\"data row5 col0\" >830.7176</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow5_col1\" class=\"data row5 col1\" >6423604.0184</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow5_col2\" class=\"data row5 col2\" >2534.4830</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9452</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.0810</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow6_col0\" class=\"data row6 col0\" >799.9136</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow6_col1\" class=\"data row6 col1\" >3353992.2636</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow6_col2\" class=\"data row6 col2\" >1831.3908</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9685</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0793</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0585</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow7_col0\" class=\"data row7 col0\" >714.3607</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow7_col1\" class=\"data row7 col1\" >1930222.6458</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow7_col2\" class=\"data row7 col2\" >1389.3245</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9756</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0732</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0556</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow8_col0\" class=\"data row8 col0\" >784.7648</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow8_col1\" class=\"data row8 col1\" >2211933.1546</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow8_col2\" class=\"data row8 col2\" >1487.2569</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9783</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.0766</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0582</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow9_col0\" class=\"data row9 col0\" >778.3590</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow9_col1\" class=\"data row9 col1\" >3127561.3571</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow9_col2\" class=\"data row9 col2\" >1768.4913</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9702</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.0872</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0609</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow10_col0\" class=\"data row10 col0\" >752.6446</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow10_col1\" class=\"data row10 col1\" >3056347.8515</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow10_col2\" class=\"data row10 col2\" >1687.9907</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9711</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.0773</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow11_col0\" class=\"data row11 col0\" >69.3829</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow11_col1\" class=\"data row11 col1\" >1661349.5128</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow11_col2\" class=\"data row11 col2\" >455.0112</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0119</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0055</td>\n",
       "                        <td id=\"T_7d172e30_2f66_11eb_b79a_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0030</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb4105d088>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightgbm = create_model('lightgbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j8DvIuOrzZsm"
   },
   "source": [
    "### 8.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:50.364299Z",
     "start_time": "2020-04-23T13:22:50.002325Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "1Y_Czm6xzZsr",
    "outputId": "df27ebb4-257b-440e-cd9b-de05ba0c0f35"
   },
   "outputs": [
    {
     "data": {
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       "            background:  yellow;\n",
       "        }    #T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_7d55e276_2f66_11eb_8403_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow0_col0\" class=\"data row0 col0\" >859.1907</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow0_col1\" class=\"data row0 col1\" >2456840.0599</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow0_col2\" class=\"data row0 col2\" >1567.4310</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.9730</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.1016</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0727</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow1_col0\" class=\"data row1 col0\" >1122.9409</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow1_col1\" class=\"data row1 col1\" >9852564.2047</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow1_col2\" class=\"data row1 col2\" >3138.8795</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.9245</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.1102</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.0758</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow2_col0\" class=\"data row2 col0\" >911.3452</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow2_col1\" class=\"data row2 col1\" >2803662.6885</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow2_col2\" class=\"data row2 col2\" >1674.4141</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9743</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.0988</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0729</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow3_col0\" class=\"data row3 col0\" >1002.5575</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow3_col1\" class=\"data row3 col1\" >3926739.3726</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow3_col2\" class=\"data row3 col2\" >1981.6002</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9567</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.1049</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0772</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow4_col0\" class=\"data row4 col0\" >1167.8154</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow4_col1\" class=\"data row4 col1\" >9751516.1909</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow4_col2\" class=\"data row4 col2\" >3122.7418</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.8784</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.1226</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0876</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow5_col0\" class=\"data row5 col0\" >1047.7778</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow5_col1\" class=\"data row5 col1\" >7833770.7037</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow5_col2\" class=\"data row5 col2\" >2798.8874</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9331</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.1128</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0791</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow6_col0\" class=\"data row6 col0\" >1010.0816</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow6_col1\" class=\"data row6 col1\" >3989282.4802</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow6_col2\" class=\"data row6 col2\" >1997.3188</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9625</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.1106</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0803</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow7_col0\" class=\"data row7 col0\" >846.8085</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow7_col1\" class=\"data row7 col1\" >2182534.9007</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow7_col2\" class=\"data row7 col2\" >1477.3405</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9724</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0933</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0709</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow8_col0\" class=\"data row8 col0\" >1001.8451</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow8_col1\" class=\"data row8 col1\" >4904945.0821</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow8_col2\" class=\"data row8 col2\" >2214.7111</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9518</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.1053</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0734</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow9_col0\" class=\"data row9 col0\" >1060.8742</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow9_col1\" class=\"data row9 col1\" >5354347.6956</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow9_col2\" class=\"data row9 col2\" >2313.9463</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9490</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.1230</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0847</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col0\" class=\"data row10 col0\" >1003.1237</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col1\" class=\"data row10 col1\" >5305620.3379</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col2\" class=\"data row10 col2\" >2228.7271</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9476</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.1083</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0775</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d55e276_2f66_11eb_8403_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow11_col0\" class=\"data row11 col0\" >100.2165</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow11_col1\" class=\"data row11 col1\" >2734194.7557</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow11_col2\" class=\"data row11 col2\" >581.7181</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0280</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0091</td>\n",
       "                        <td id=\"T_7d55e276_2f66_11eb_8403_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0052</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb3e401dc8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = create_model('dt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NsOBIl8szZs1"
   },
   "source": [
    "Notice that the Mean score of all models matches with the score printed in `compare_models()`. This is because the metrics printed in the `compare_models()` score grid are the average scores across all CV folds. Similar to `compare_models()`, if you want to change the fold parameter from the default value of 10 to a different value then you can use the `fold` parameter. For Example: `create_model('dt', fold = 5)` to create Decision Tree using 5 fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8RZB8YllzZs7"
   },
   "source": [
    "# 9.0 Tune a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AYYWC1X5zZs-"
   },
   "source": [
    "When a model is created using the `create_model` function it uses the default hyperparameters to train the model. In order to tune hyperparameters, the `tune_model` function is used. This function automatically tunes the hyperparameters of a model using `Random Grid Search` on a pre-defined search space. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. To use the custom search grid, you can pass `custom_grid` parameter in the `tune_model` function (see 9.2 LightGBM tuning below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uUSmZLGzZtB"
   },
   "source": [
    "### 9.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:23.226986Z",
     "start_time": "2020-04-23T13:25:17.516114Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "XM7qgcGIzZtE",
    "outputId": "e87c7fac-4dfe-4733-ddc5-d44359b0ea0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
       "        }    #T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow0_col0\" class=\"data row0 col0\" >2759.6611</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow0_col1\" class=\"data row0 col1\" >16380711.7542</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow0_col2\" class=\"data row0 col2\" >4047.3092</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.8202</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.2808</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.2613</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow1_col0\" class=\"data row1 col0\" >2901.3392</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow1_col1\" class=\"data row1 col1\" >22572903.5720</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow1_col2\" class=\"data row1 col2\" >4751.0950</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8270</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.3087</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.2942</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow2_col0\" class=\"data row2 col0\" >2791.3101</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow2_col1\" class=\"data row2 col1\" >15829783.4301</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow2_col2\" class=\"data row2 col2\" >3978.6660</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.8548</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.3135</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.3086</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow3_col0\" class=\"data row3 col0\" >2622.3949</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow3_col1\" class=\"data row3 col1\" >14647515.3862</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow3_col2\" class=\"data row3 col2\" >3827.2073</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.8384</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.2717</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.2334</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow4_col0\" class=\"data row4 col0\" >2457.5786</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow4_col1\" class=\"data row4 col1\" >14562994.4838</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow4_col2\" class=\"data row4 col2\" >3816.1492</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.8184</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.2671</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.2264</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow5_col0\" class=\"data row5 col0\" >2686.6949</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow5_col1\" class=\"data row5 col1\" >19471487.9107</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow5_col2\" class=\"data row5 col2\" >4412.6509</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.8338</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.3001</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.2783</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow6_col0\" class=\"data row6 col0\" >2780.2231</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow6_col1\" class=\"data row6 col1\" >17357834.7679</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow6_col2\" class=\"data row6 col2\" >4166.2735</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.8369</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.3054</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.2822</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow7_col0\" class=\"data row7 col0\" >2898.4723</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow7_col1\" class=\"data row7 col1\" >18031683.4258</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow7_col2\" class=\"data row7 col2\" >4246.3730</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7720</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.3076</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.2929</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow8_col0\" class=\"data row8 col0\" >2593.3611</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow8_col1\" class=\"data row8 col1\" >15134670.4021</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow8_col2\" class=\"data row8 col2\" >3890.3304</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.8514</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.2964</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.2719</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow9_col0\" class=\"data row9 col0\" >2533.0852</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow9_col1\" class=\"data row9 col1\" >15820915.8817</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow9_col2\" class=\"data row9 col2\" >3977.5515</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8494</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.2943</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.2714</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col0\" class=\"data row10 col0\" >2702.4121</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col1\" class=\"data row10 col1\" >16981050.1015</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col2\" class=\"data row10 col2\" >4111.3606</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.8302</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.2946</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.2721</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow11_col0\" class=\"data row11 col0\" >142.2686</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow11_col1\" class=\"data row11 col1\" >2375923.6205</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow11_col2\" class=\"data row11 col2\" >278.8624</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0228</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0153</td>\n",
       "                        <td id=\"T_8a2c3aee_2f66_11eb_8810_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0247</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb25233788>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_ada = tune_model(ada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:31.211932Z",
     "start_time": "2020-04-23T13:25:31.208661Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "Ul0HJFoRzZtU",
    "outputId": "5ac4ab0b-c746-4a9f-a286-3e5cd2bbe536"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=0.175, loss='exponential',\n",
      "                  n_estimators=60, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "print(tuned_ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kvdvfdUzZtj"
   },
   "source": [
    "### 9.2 Light Gradient Boosting Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "lgbm_params = {'num_leaves': np.arange(10,200,10),\n",
    "                        'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],\n",
    "                        'learning_rate': np.arange(0.1,1,0.1)\n",
    "                        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:35.696328Z",
     "start_time": "2020-04-23T13:25:33.725433Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "s1agvmDFzZtm",
    "outputId": "7cfab1a7-e7c2-40df-ad1e-a40067bcc4e0"
   },
   "outputs": [
    {
     "data": {
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       "                <tr>\n",
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       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow1_col0\" class=\"data row1 col0\" >785.8158</td>\n",
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       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow2_col0\" class=\"data row2 col0\" >808.0977</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow2_col1\" class=\"data row2 col1\" >3024520.4058</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow2_col2\" class=\"data row2 col2\" >1739.1148</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9723</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.0836</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0597</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow3_col0\" class=\"data row3 col0\" >749.7881</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow3_col1\" class=\"data row3 col1\" >1774260.2775</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow3_col2\" class=\"data row3 col2\" >1332.0136</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9804</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.0724</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0556</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow4_col0\" class=\"data row4 col0\" >694.0351</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow4_col1\" class=\"data row4 col1\" >1974576.4174</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow4_col2\" class=\"data row4 col2\" >1405.1962</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.9754</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.0838</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0585</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow5_col0\" class=\"data row5 col0\" >841.6462</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow5_col1\" class=\"data row5 col1\" >6725524.0654</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow5_col2\" class=\"data row5 col2\" >2593.3615</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9426</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.0824</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0582</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow6_col0\" class=\"data row6 col0\" >796.0240</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow6_col1\" class=\"data row6 col1\" >3324498.6208</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow6_col2\" class=\"data row6 col2\" >1823.3208</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9688</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0774</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0564</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow7_col0\" class=\"data row7 col0\" >713.1006</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow7_col1\" class=\"data row7 col1\" >1872493.1136</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow7_col2\" class=\"data row7 col2\" >1368.3907</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9763</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0715</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0551</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow8_col0\" class=\"data row8 col0\" >775.9760</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow8_col1\" class=\"data row8 col1\" >2274682.3424</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow8_col2\" class=\"data row8 col2\" >1508.2050</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9777</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.0766</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0579</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow9_col0\" class=\"data row9 col0\" >768.3451</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow9_col1\" class=\"data row9 col1\" >3247098.5445</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow9_col2\" class=\"data row9 col2\" >1801.9707</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9691</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.0885</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0594</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow10_col0\" class=\"data row10 col0\" >758.2083</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow10_col1\" class=\"data row10 col1\" >3086711.2059</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow10_col2\" class=\"data row10 col2\" >1698.4210</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9708</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.0781</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow11_col0\" class=\"data row11 col0\" >54.9147</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow11_col1\" class=\"data row11 col1\" >1678033.7774</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow11_col2\" class=\"data row11 col2\" >449.5301</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0120</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0058</td>\n",
       "                        <td id=\"T_8e8f2c28_2f66_11eb_971d_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0023</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb3ea5f6c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_lightgbm = tune_model(lightgbm, custom_grid = lgbm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              importance_type='split', learning_rate=0.1, max_depth=60,\n",
      "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
      "              n_estimators=100, n_jobs=-1, num_leaves=120, objective=None,\n",
      "              random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
      "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n"
     ]
    }
   ],
   "source": [
    "print(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ovz73MkgzZtx"
   },
   "source": [
    "### 9.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:38.913881Z",
     "start_time": "2020-04-23T13:25:38.267918Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "kFImcOpXzZt0",
    "outputId": "36165e01-0b2e-4fa4-efcc-5efa70a3236f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow0_col0\" class=\"data row0 col0\" >1000.7122</td>\n",
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       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow1_col0\" class=\"data row1 col0\" >1080.2841</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow1_col1\" class=\"data row1 col1\" >6686388.0416</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow1_col2\" class=\"data row1 col2\" >2585.8051</td>\n",
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       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.1053</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow2_col0\" class=\"data row2 col0\" >1002.3163</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow2_col1\" class=\"data row2 col1\" >3275429.6329</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow2_col2\" class=\"data row2 col2\" >1809.8148</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9700</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.1051</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0812</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow3_col0\" class=\"data row3 col0\" >1080.7850</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow3_col1\" class=\"data row3 col1\" >4037154.5985</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow3_col2\" class=\"data row3 col2\" >2009.2672</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9555</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.1172</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0870</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow4_col0\" class=\"data row4 col0\" >1101.6333</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow4_col1\" class=\"data row4 col1\" >7889520.5391</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow4_col2\" class=\"data row4 col2\" >2808.8290</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.9016</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.1189</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0842</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow5_col0\" class=\"data row5 col0\" >1275.5901</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow5_col1\" class=\"data row5 col1\" >11021312.1970</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow5_col2\" class=\"data row5 col2\" >3319.8362</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9059</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.1250</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0895</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow6_col0\" class=\"data row6 col0\" >1068.6534</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow6_col1\" class=\"data row6 col1\" >4463866.3029</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow6_col2\" class=\"data row6 col2\" >2112.7864</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9581</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.1076</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0809</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow7_col0\" class=\"data row7 col0\" >975.9364</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow7_col1\" class=\"data row7 col1\" >3271028.5175</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow7_col2\" class=\"data row7 col2\" >1808.5985</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9586</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.1099</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0807</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow8_col0\" class=\"data row8 col0\" >1101.9207</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow8_col1\" class=\"data row8 col1\" >4441966.3616</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow8_col2\" class=\"data row8 col2\" >2107.5973</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9564</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.1114</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0873</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow9_col0\" class=\"data row9 col0\" >1065.1662</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow9_col1\" class=\"data row9 col1\" >5192339.2748</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow9_col2\" class=\"data row9 col2\" >2278.6705</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9506</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.1224</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0873</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow10_col0\" class=\"data row10 col0\" >1075.2997</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow10_col1\" class=\"data row10 col1\" >5317416.4597</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow10_col2\" class=\"data row10 col2\" >2254.2722</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9474</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.1130</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0842</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow11_col0\" class=\"data row11 col0\" >79.0463</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow11_col1\" class=\"data row11 col1\" >2416581.2427</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow11_col2\" class=\"data row11 col2\" >485.4621</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0227</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0069</td>\n",
       "                        <td id=\"T_9069d8d8_2f66_11eb_93b2_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0031</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb355488c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_dt = tune_model(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TcSjA7SUzZt-"
   },
   "source": [
    "By default, `tune_model` optimizes `R2` but this can be changed using optimize parameter. For example: tune_model(dt, optimize = 'MAE') will search for the hyperparameters of a Decision Tree Regressor that results in the lowest `MAE` instead of highest `R2`. For the purposes of this example, we have used the default metric `R2` for the sake of simplicity only. The methodology behind selecting the right metric to evaluate a regressor is beyond the scope of this tutorial but if you would like to learn more about it, you can __[click here](https://www.dataquest.io/blog/understanding-regression-error-metrics/)__ to develop an understanding on regression error metrics.\n",
    "\n",
    "Metrics alone are not the only criteria you should consider when finalizing the best model for production. Other factors to consider include training time, standard deviation of k-folds etc. As you progress through the tutorial series we will discuss those factors in detail at the intermediate and expert levels. For now, let's move forward considering the Tuned Light Gradient Boosting Machine stored in the `tuned_lightgbm` variable as our best model for the remainder of this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HR-mHgtCzZuE"
   },
   "source": [
    "# 10.0 Plot a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N6i7Ggg_zZuH"
   },
   "source": [
    "Before model finalization, the `plot_model()` function can be used to analyze the performance across different aspects such as Residuals Plot, Prediction Error, Feature Importance etc. This function takes a trained model object and returns a plot based on the test / hold-out set. \n",
    "\n",
    "There are over 10 plots available, please see the `plot_model()` docstring for the list of available plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HJCYRQj9zZuU"
   },
   "source": [
    "### 10.1 Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:47.857901Z",
     "start_time": "2020-04-23T13:29:47.261824Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "ml-qe8dTzZuX",
    "outputId": "4c69b0b8-8e82-4003-f0e2-158b19a79f34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rM9dWgfVzZuh"
   },
   "source": [
    "### 10.2 Prediction Error Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:53.402735Z",
     "start_time": "2020-04-23T13:29:53.197809Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "GPwWRYehzZuk",
    "outputId": "e3dfe255-fa08-42f7-e556-74c7909f6e6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot = 'error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWu_EtTGzZuu"
   },
   "source": [
    "### 10.3 Feature Importance Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:57.854731Z",
     "start_time": "2020-04-23T13:29:57.758312Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "7Yh852PPzZux",
    "outputId": "38295169-5000-4a07-e71d-76de9877ab42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot='feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qI_tk-8RzZu8"
   },
   "source": [
    "*Another* way to analyze the performance of models is to use the `evaluate_model()` function which displays a user interface for all of the available plots for a given model. It internally uses the `plot_model()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:30:17.357283Z",
     "start_time": "2020-04-23T13:30:17.326916Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398,
     "referenced_widgets": [
      "2f905c9057e849cfb68d8b4a73a9ae2c",
      "4705ae97c5c341db8a7efcc7e4334c79",
      "202f5f0dfbea4f6cb6a75e4aebd370c7",
      "4c0b5f15356140e9bd6ed3a2e0850baa",
      "63e7b6e04e3d43118698abaca0973960",
      "b7731479f5f141289a4939cb73adfb28",
      "5eb7949ac4a140118e9ee6be49921284",
      "19ea86ac89a349e281011fbd327c29c0",
      "c9bbc67e75d1477e8cd7a64502b98704",
      "5334e09f4bed4a0ba45635300e76027e",
      "14244d0fca4f40a8b3552675869748a3",
      "baf068abf86f44a9a9502f79dfe17eb5",
      "3495c4e24f1d4b22af1117ea2403a4fa",
      "83a10da0205742d9a383671a0be02606",
      "4ae774bce02d4c1ab1351cd75b25cf17",
      "ee5416de1bb1462e87b2fa6f9065d646",
      "36a9c708b65e498586a07d526e6a5d06",
      "aeb87dce945f4d588807575e9f6e64c3"
     ]
    },
    "colab_type": "code",
    "id": "J4ryBACHzZu_",
    "outputId": "246ab553-8043-4cbe-fe99-7ea00bb13aed"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d753265ac9341e49ee65f524886d5fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CxKARgKAzZvJ"
   },
   "source": [
    "# 11.0 Predict on Test / Hold-out Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8k_rmplzZvL"
   },
   "source": [
    "Before finalizing the model, it is advisable to perform one final check by predicting the test/hold-out set and reviewing the evaluation metrics. If you look at the information grid in Section 6 above, you will see that 30% (1621 samples) of the data has been separated out as a test/hold-out sample. All of the evaluation metrics we have seen above are cross-validated results based on training set (70%) only. Now, using our final trained model stored in the `tuned_lightgbm` variable we will predict the hold-out sample and evaluate the metrics to see if they are materially different than the CV results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:31:47.571061Z",
     "start_time": "2020-04-23T13:31:47.543390Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ozTyeSjCzZvY",
    "outputId": "1cdf25f4-0988-40fa-c8e9-5695fec11f05"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col1\" class=\"data row0 col1\" >781.5572</td>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col2\" class=\"data row0 col2\" >3816757.2761</td>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col3\" class=\"data row0 col3\" >1953.6523</td>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.9652</td>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0787</td>\n",
       "                        <td id=\"T_91d84ae8_2f66_11eb_9a62_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.0558</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb4111ab88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(tuned_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UouOXHaxzZvo"
   },
   "source": [
    "The R2 on the test/hold-out set is **`0.9652`** compared to **`0.9708`** achieved on `tuned_lightgbm` CV results (in section 9.2 above). This is not a significant difference. If there is a large variation between the test/hold-out and CV results, then this would normally indicate over-fitting but could also be due to several other factors and would require further investigation. In this case, we will move forward with finalizing the model and predicting on unseen data (the 10% that we had separated in the beginning and never exposed to PyCaret).\n",
    "\n",
    "(TIP : It's always good to look at the standard deviation of CV results when using `create_model`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0PmhEQFzZvr"
   },
   "source": [
    "# 12.0 Finalize Model for Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rtaj0uWgzZvx"
   },
   "source": [
    "Model finalization is the last step in the experiment. A normal machine learning workflow in PyCaret starts with `setup()`, followed by comparing all models using `compare_models()` and shortlisting a few candidate models (based on the metric of interest) to perform several modeling techniques such as hyperparameter tuning, ensembling, stacking etc. This workflow will eventually lead you to the best model for use in making predictions on new and unseen data. The `finalize_model()` function fits the model onto the complete dataset including the test/hold-out sample (30% in this case). The purpose of this function is to train the model on the complete dataset before it is deployed in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:33:51.300384Z",
     "start_time": "2020-04-23T13:33:51.215142Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "UPk310pezZv0"
   },
   "outputs": [],
   "source": [
    "final_lightgbm = finalize_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:33:52.258663Z",
     "start_time": "2020-04-23T13:33:52.255298Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IGFUGDAPzZwF",
    "outputId": "ddaa33c4-f728-444a-fc33-47da5c37304a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              importance_type='split', learning_rate=0.1, max_depth=60,\n",
      "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
      "              n_estimators=100, n_jobs=-1, num_leaves=120, objective=None,\n",
      "              random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
      "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n"
     ]
    }
   ],
   "source": [
    "print(final_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmJjaIkrzZwQ"
   },
   "source": [
    "**Caution:** One final word of caution. Once the model is finalized using `finalize_model()`, the entire dataset including the test/hold-out set is used for training. As such, if the model is used for predictions on the hold-out set after `finalize_model()` is used, the information grid printed will be misleading as you are trying to predict on the same data that was used for modeling. In order to demonstrate this point only, we will use `final_lightgbm` under `predict_model()` to compare the information grid with the one above in section 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:34:02.730481Z",
     "start_time": "2020-04-23T13:34:02.704786Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bmYJRTAyzZwU",
    "outputId": "58e2f57b-abda-4166-c82b-af52ab6f18b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col1\" class=\"data row0 col1\" >459.9160</td>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col2\" class=\"data row0 col2\" >1199892.0334</td>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col3\" class=\"data row0 col3\" >1095.3958</td>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.9891</td>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0498</td>\n",
       "                        <td id=\"T_d925a2a8_2f66_11eb_ba90_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.0362</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bb2530a508>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(final_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5NkpL1ZHzZwr"
   },
   "source": [
    "Notice how the R2 in the `final_lightgbm` has increased to **`0.9891`** from **`0.9652`**, even though the model is same. This is because the `final_lightgbm` variable is trained on the complete dataset including the test/hold-out set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CgKSkSsZzZwv"
   },
   "source": [
    "# 13.0 Predict on Unseen Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6n7QFM94zZwy"
   },
   "source": [
    "The `predict_model()` function is also used to predict on the unseen dataset. The only difference from section 11 above is that this time we will pass the `data_unseen` parameter. `data_unseen` is the variable created at the beginning of the tutorial and contains 10% (600 samples) of the original dataset which was never exposed to PyCaret. (see section 5 for explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YdlpJUx0zZw4",
    "outputId": "5b45a2b5-c9a1-4d20-80f7-28211f07d586"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.53</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>12791</td>\n",
       "      <td>12262.949782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Fair</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10450</td>\n",
       "      <td>10122.442382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5161</td>\n",
       "      <td>5032.520456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.51</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>34361</td>\n",
       "      <td>34840.379469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4238</td>\n",
       "      <td>4142.695964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.53      Ideal     E     SI1     ID       ID   AGSL  12791   \n",
       "1          1.50       Fair     F     SI1     VG       VG    GIA  10450   \n",
       "2          1.01       Good     E     SI1      G        G    GIA   5161   \n",
       "3          2.51  Very Good     G     VS2     VG       VG    GIA  34361   \n",
       "4          1.01       Good     I     SI1     VG       VG    GIA   4238   \n",
       "\n",
       "          Label  \n",
       "0  12262.949782  \n",
       "1  10122.442382  \n",
       "2   5032.520456  \n",
       "3  34840.379469  \n",
       "4   4142.695964  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_lightgbm, data=data_unseen)\n",
    "unseen_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZnpuHoDzZxG"
   },
   "source": [
    "The `Label` column is added onto the `data_unseen` set. Label is the predicted value using the `final_lightgbm` model. If you want predictions to be rounded, you can use `round` parameter inside `predict_model()`. You can also check the metrics on this since you have actual target column `Price` available. To do that we will use pycaret.utils module. See example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9779"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.utils import check_metric\n",
    "check_metric(unseen_predictions.Price, unseen_predictions.Label, 'R2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "os2dbiIrzZxJ"
   },
   "source": [
    "# 14.0 Saving the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "46CV19RlzZxL"
   },
   "source": [
    "We have now finished the experiment by finalizing the `tuned_lightgbm` model which is now stored in `final_lightgbm` variable. We have also used the model stored in `final_lightgbm` to predict `data_unseen`. This brings us to the end of our experiment, but one question is still to be asked: What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is no, PyCaret's inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tXl6hkG9zZxN",
    "outputId": "4af19b19-6c0c-4b1e-a4f7-249e729091bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=True, features_todrop=[],\n",
       "                                       id_columns=[], ml_usecase='regression',\n",
       "                                       numerical_features=[], target='Price',\n",
       "                                       time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='not_available',\n",
       "                                 fill_value_categorical=None,\n",
       "                                 fill_value_numerical=None,\n",
       "                                 numeric_strategy='...\n",
       "                  LGBMRegressor(boosting_type='gbdt', class_weight=None,\n",
       "                                colsample_bytree=1.0, importance_type='split',\n",
       "                                learning_rate=0.1, max_depth=60,\n",
       "                                min_child_samples=20, min_child_weight=0.001,\n",
       "                                min_split_gain=0.0, n_estimators=100, n_jobs=-1,\n",
       "                                num_leaves=120, objective=None, random_state=123,\n",
       "                                reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "                                subsample=1.0, subsample_for_bin=200000,\n",
       "                                subsample_freq=0)]],\n",
       "          verbose=False), 'Final LightGBM Model 25Nov2020.pkl')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_model(final_lightgbm,'Final LightGBM Model 25Nov2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q2wgdZ5ozZxX"
   },
   "source": [
    "(TIP : It's always good to use date in the filename when saving models, it's good for version control.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LsyznpCzZxb"
   },
   "source": [
    "# 15.0 Loading the Saved Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ZH-4EMLzZxd"
   },
   "source": [
    "To load a saved model at a future date in the same or an alternative environment, we would use PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2hsqdgn3zZxg",
    "outputId": "9db6b97b-ea93-452b-e4b4-121bea203bd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "saved_final_lightgbm = load_model('Final LightGBM Model 25Nov2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NBAXt62nzZx5"
   },
   "source": [
    "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function. Below we have applied the loaded model to predict the same `data_unseen` that we used in section 13 above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y7debJpCzZx8"
   },
   "outputs": [],
   "source": [
    "new_prediction = predict_model(saved_final_lightgbm, data=data_unseen)"
   ]
  },
  {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.53</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>12791</td>\n",
       "      <td>12262.949782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Fair</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10450</td>\n",
       "      <td>10122.442382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5161</td>\n",
       "      <td>5032.520456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.51</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>34361</td>\n",
       "      <td>34840.379469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4238</td>\n",
       "      <td>4142.695964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.53      Ideal     E     SI1     ID       ID   AGSL  12791   \n",
       "1          1.50       Fair     F     SI1     VG       VG    GIA  10450   \n",
       "2          1.01       Good     E     SI1      G        G    GIA   5161   \n",
       "3          2.51  Very Good     G     VS2     VG       VG    GIA  34361   \n",
       "4          1.01       Good     I     SI1     VG       VG    GIA   4238   \n",
       "\n",
       "          Label  \n",
       "0  12262.949782  \n",
       "1  10122.442382  \n",
       "2   5032.520456  \n",
       "3  34840.379469  \n",
       "4   4142.695964  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_prediction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cuVEPftKzZyK"
   },
   "source": [
    "Notice that the results of `unseen_predictions` and `new_prediction` are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9779"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.utils import check_metric\n",
    "check_metric(new_prediction.Price, new_prediction.Label, 'R2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   "source": [
    "# 16.0 Wrap-up / Next Steps?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ct8oOjuvzZyS"
   },
   "source": [
    "This tutorial has covered the entire machine learning pipeline from data ingestion, pre-processing, training the model, hyperparameter tuning, prediction and saving the model for later use. We have completed all of these steps in less than 10 commands which are naturally constructed and very intuitive to remember such as `create_model()`, `tune_model()`, `compare_models()`. Re-creating the entire experiment without PyCaret would have taken well over 100 lines of code in most libraries.\n",
    "\n",
    "We have only covered the basics of `pycaret.regression`. In following tutorials we will go deeper into advanced pre-processing, ensembling, generalized stacking and other techniques that allow you to fully customize your machine learning pipeline and are must know for any data scientist.\n",
    "\n",
    "See you at the next tutorial. Follow the link to __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__"
   ]
  }
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xHqY/gC3BpqZXgOh67efNfBF3SgqvtehJsj+I37KRtsSha2S4dDKSHGT36oSs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+W/+wMNqzYSWF9PzHaMm9e362u2Ri+lSneG8LqNPU9/TZNFD/6LI+/9WZev18/6tItBB4cZvG1l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F+Zz/6F3ofVAWv3D5jSx6+OmuYiLDpHbxp9wSHM1Vn73DWQ/+jjPvv52P7nuU1S/P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\ncJIIqlZ/jN16SUG03E1LgWMrZAdZqCkgZEjMSIR0j0GeR2K28sZ2HMVLD/6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        "metadata": {
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 576x396 with 2 Axes>"
       }
      ]
     }
    },
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     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "Processing: ",
      "description_tooltip": null,
      "layout": "IPY_MODEL_baf068abf86f44a9a9502f79dfe17eb5",
      "max": 5,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_14244d0fca4f40a8b3552675869748a3",
      "value": 4
     }
    },
    "5eb7949ac4a140118e9ee6be49921284": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
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      "_view_name": "ProgressView",
      "bar_style": "",
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